Seismic Hazard Analysis Based on Fuzzy-Probabilistic Approach

Andrić, Jelena M.; Lü, Dagang

doi:10.7712/120117.5739.17539

Cited by 1 publication

(2 citation statements)

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“…The goal of the fuzzy theory is to solve high uncertainty issues, to deal with ambiguous, chaotic and conflicting information, and to handle imprecise data possessing non-statistical uncertainties [8][9][10][11]. Fuzzy-probability approach has been verified as the efficient and practical tool in seismic resilience assessment [12,13], risk assessment in construction industry [13], flood risk assessment [14], risk assessment for oil and gas [15], safety assessment of structures [16], human health risk assessment [17], and design of water distribution network [18].…”

Section: Introductionmentioning

confidence: 99%

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Selection of ground motion prediction equations for probabilistic seismic hazard analysis based on an improved fuzzy logic

Shayanfar¹,

Barkhordari²,

Mahmoudi³

et al. 2019

J. vibroeng.

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The fuzzy logic method has been used widely in civil and earthquake engineering, but there is no comprehensive point of view for utilizing fuzzy approach in order to obtain ground motion prediction equations (GMPEs) for probabilistic seismic hazard analysis (PSHA). Hence, fuzzy magnitude-distance method as a new approach for choosing GMPEs in the process of PSHA, is developed in this research through the selection of the ruling peak ground acceleration (PGA) of each common cell (the combined cell of earthquake intensity and site to source distance). The presented method reduces the need for engineering judgments in seismic analysis based on a newly developed benchmark. It enables designers to not only determine the range of acceptable fuzzy results but also introduces a concept which ensures the selection of initial well-suited GMPEs for the analysis.

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Section: Introductionmentioning

confidence: 99%

“…FPSHA has recently been developed in various aspects. Andric (2017) proposed a method which source-to-site distance and earthquake magnitude was defined as fuzzy-random variables [11]. Considering two types of uncertainties, including aleatory and epistemic, was the main advantage of the proposed model over the classic PSHA.…”

Section: Introductionmentioning

confidence: 99%